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Exploring the spatially-resolved capabilities of the J-PAS survey with Py2DJPAS

J. E. Rodríguez-Martín, L. A. Díaz-García, R. M. González Delgado, G. Martínez-Solaeche, R. García-Benito, A. de Amorim, J. Thainá-Batista, R. Cid Fernandes, I. Márquez, A. Fernández-Soto, I. Breda, R. Abramo, J. Alcaniz, N. Benítez, S. Bonoli, S. Carneiro, A. J. Cenarro, D. Cristóbal-Hornillos, R. A. Dupke, A. Ederoclite, A. Hernán-Caballero, C. Hernández-Monteagudo, C. López-Sanjuan, A. Marín-Franch, C. Mendes de Oliveira, M. Moles, L. Sodré, K. Taylor, J. Varela, H. Vázquez Ramió

TL;DR

This work presents a tool developed in to automate the analysis of the properties of spatially resolved galaxies in the survey, a 1 deg^2 survey that acts as precursor of the Javalambre Physics of the Acclerating Universe Survey and demonstrates the IFU-like capabilities of J-PAS.

Abstract

We present Py2DJPAS, a Python-based tool to automate the analysis of spatially resolved galaxies in the \textbf{miniJPAS} survey, a 1~deg$^2$ precursor of the J-PAS survey, using the same filter system, telescope, and Pathfinder camera. Py2DJPAS streamlines the entire workflow: downloading scientific images and catalogs, performing PSF homogenization, masking, aperture definition, SED fitting, and estimating optical emission line equivalent widths via an artificial neural network. We validate Py2DJPAS on a sample of resolved miniJPAS galaxies, recovering magnitudes in all bands consistent with the catalog ($\sim 10$~\% precision using SExtractor). Local background estimation improves results for faint galaxies and apertures. PSF homogenization enables consistent multi-band photometry in inner apertures, allowing pseudo-spectra generation without artifacts. SED fitting across annular apertures yields residuals $<10$~\%, with no significant wavelength-dependent bias for regions with $S/N>5$. We demonstrate the IFU-like capability of J-PAS by analyzing the spatially resolved properties of galaxy 2470-10239 at $z = 0.078$, comparing them to MaNGA data within 1 half-light radius (HLR). We find excellent agreement in photometric vs. spectroscopic measurements and stellar mass surface density profiles. Our analysis extends to 4 HLR (S/N~$\sim$~5), showing that J-PAS can probe galaxy outskirts, enabling the study of evolutionary processes at large galactocentric distances.

Exploring the spatially-resolved capabilities of the J-PAS survey with Py2DJPAS

TL;DR

This work presents a tool developed in to automate the analysis of the properties of spatially resolved galaxies in the survey, a 1 deg^2 survey that acts as precursor of the Javalambre Physics of the Acclerating Universe Survey and demonstrates the IFU-like capabilities of J-PAS.

Abstract

We present Py2DJPAS, a Python-based tool to automate the analysis of spatially resolved galaxies in the \textbf{miniJPAS} survey, a 1~deg precursor of the J-PAS survey, using the same filter system, telescope, and Pathfinder camera. Py2DJPAS streamlines the entire workflow: downloading scientific images and catalogs, performing PSF homogenization, masking, aperture definition, SED fitting, and estimating optical emission line equivalent widths via an artificial neural network. We validate Py2DJPAS on a sample of resolved miniJPAS galaxies, recovering magnitudes in all bands consistent with the catalog (~\% precision using SExtractor). Local background estimation improves results for faint galaxies and apertures. PSF homogenization enables consistent multi-band photometry in inner apertures, allowing pseudo-spectra generation without artifacts. SED fitting across annular apertures yields residuals ~\%, with no significant wavelength-dependent bias for regions with . We demonstrate the IFU-like capability of J-PAS by analyzing the spatially resolved properties of galaxy 2470-10239 at , comparing them to MaNGA data within 1 half-light radius (HLR). We find excellent agreement in photometric vs. spectroscopic measurements and stellar mass surface density profiles. Our analysis extends to 4 HLR (S/N~~5), showing that J-PAS can probe galaxy outskirts, enabling the study of evolutionary processes at large galactocentric distances.

Paper Structure

This paper contains 27 sections, 4 equations, 13 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Data
  3. The miniJPAS survey
  4. Sample selection
  5. Methodology
  6. Pre-processing
  7. Py2DJPAS
  8. SED-fitting and ANN estimation
  9. Results
  10. PSF homogenisation and masking
  11. PSF homogenisation effect on the J-spectra
  12. Photometry measurements
  13. Impact of the background subtraction
  14. Photometry comparison
  15. Spatially resolved properties: R_EFF vs AUTO aperture
  16. ...and 12 more sections

Figures (13)

  • Figure 1: Summary of the image treatment process. Left panel shows the $r_\mathrm{SDSS}$ image of the galaxy $2470-10239$ with no treatment. The middle panel shows the $r_\mathrm{SDSS}$ image after PSF homogenisation. Right panel shows the final image, after PSF homogenisation and applying the sources mask. The colour scale indicates the flux in ADUs.
  • Figure 2: Flux comparison of the J-spectra of different apertures of the galaxy $2470-10239$ before and after applying the PSF homogenisation for different apertures, from left to right: R$<0.25$R_EFF, $0.25$R_EFF$<$R$<0.5$R_EFF, and $1.5$R_EFF$<$R$<2$R_EFF. Red points represent the J-spectra obtained with the images prior to PSF homogenisation. Black point represent the J-spectra after all the images have been degraded to the worst PSF.
  • Figure 3: Comparison of the elliptical photometry available in the catalogue and the values retrieved with Py2DJPAS for the galaxy 2241--7608, before and after background correction. Blue points represent the values obtained when no background subtraction is applied. Red points represent the values of the photometry obtained applying a background correction. Left panels show the one-to-one relation. The black dashed line shows the one-to-one relation. Middle panels show the difference between our calculations and the SExtractor catalogue values, calculated as $\Delta \mathrm{MAG} = \mathrm{MAG}_{\texttt{SExtractor}} -\mathrm{MAG}_{\texttt{Py2DJPAS}}$, as a function of wavelength. The black dashed line shows the 0 target value. Right panels show the S/N image with the aperture used for each calculation. Top row represents the comparison for MAG_AUTO. Bottom row represents the comparison for MAG_PETRO.
  • Figure 4: Comparison of the MAG_AUTO photometry of the catalogue and the one obtained with our methodology. Left panel shows the one-to-one relation. Upper right panel shows the difference of the magnitudes (SExtractor - Py2DJPAS) for each filter for each galaxy as a function of the magnitude of the band. Bottom right panel shows the difference for each filter for each galaxy as a function of the pivot wavelength of the filter. Colour scale represents the density of points. Black points represent the median value in each brightness bin and wavelength bin.
  • Figure 5: Comparison of properties at 1 R_EFF and the MAG_AUTO photometry for the complete galaxy sample. Top panel shows the stellar mass. Bottom panel shows the $(u-r)_\mathrm{int}$ colour.
  • ...and 8 more figures